1. Field of the Invention
The present invention is directed, in general, to image analysis and, more particularly, to the computerized detection and discrimination of anomalies in breast tissue images.
2. Description of the Related Art
In the year 2003 in the United States, according to estimates by the National Cancer Institute, there were 211,300 new cases of invasive breast cancer, 55,700 new cases of in situ breast cancers, and 40,200 deaths related to breast cancer. This makes breast cancer the most frequently diagnosed non-skin cancer in women and the second leading cause of cancer-related deaths in women today. There is no reliable estimate, however, of the number of missed cancer detections each year. Early detection of breast cancer greatly increases the probability of survival, and improves quality of life.
In breast cancer, incidence rates have been growing annually at a rate of about 1.1% per year, although mortality rates declined at an annual rate of 1.4% from 1989-1995 and 3.2% subsequently. Approximately 23 million mammograms are performed annually, of which, approximately 10% require additional testing. This leads to about 500,000 needle or surgical biopsies per year at a cost exceeding $1 billion per year to the health care system, with only about 30% of biopsies indicating malignant findings. This underscores the need for improved accuracy of discrimination between cancerous and non-cancerous breast masses to reduce patient trauma and costs.
Another key issue in breast cancer treatment is the number of patients in which the cancer is not completely removed in the initial surgery. Therefore, there is a need for an improved methodology for the radiologist to better define the cancerous margins.
The present invention provides an improved methodology for the radiologist to detect anomalies in the breast tissue, discriminate between cancerous and non-cancerous breast tissue, and to identify the margins of cancerous tissue.
Breast cancer detection is highly dependent on mammogram imagery. The current methodology relies primarily on visual inspection by radiologists with some support from automated computer aided design (CAD) systems. The software for most CAD systems is proprietary and uses intensity thresholding and contrast stretching for detection and shape/pattern recognition for discrimination. The current methodology has the following limitations: (1) poor detection in early stages and in high-density breast tissue; (2) many false alarms; (3) poor discrimination of cancerous masses from calcifications, non-cancerous lesions, and cysts; and (4) an inability to resolve the margins of the cancerous mass.
The present invention overcomes these limitations by providing improved detection of masses in noisy images, improved discrimination capability that reduces the number of false alarms, and improved digital visualization to aid the radiologist in defining cancerous margins.